Heart disease is still the major cause of death in industrialized societies even though lifestyle changes and effective drugs that lower LDL cholesterol have reduced its incidence. The major avenue to further therapeutic progress lies in learning how to raise HDL, a major protection against heart disease; it has been estimated that a modest increase in HDL could lead to a large decrease in heart disease incidence. The mouse is an excellent model for finding HDL genes and the proteins they encode. Not only are quantitative trait loci (QTLs) for HDL in mouse and human found in concordant locations, but the mouse model can be used to determine whether a polymorphism that raises HDL levels also reduces atherosclerosis risk. We believe that we would learn much by identifying all (or nearly all) of the genes underlying one complex trait. We suggest that HDL is the best complex trait for such an undertaking because of the substantial infrastructure of HDL QTL studies in the mouse, linkage and genome wide association studies in humans, and considerable knowledge about HDL metabolism. Any insights into this complex trait will have considerable relevance to the ways in which we think about other complex traits responsible for many of our major diseases. In this grant period, Aim 1 will be to identify 7 additional QTL genes that affect HDL using the combination of genetic and bioinformatic tools that have proven so successful during the last few years. When we identify these genes, some may have a known function in HDL metabolism; others, however, may be quite new, and the mechanisms by which they modulate HDL levels may be completely unknown. For these novel genes, it is important that we verify their role in HDL metabolism with additional evidence. Therefore in Aim 2 we propose to obtain that extra evidence by making transgenics or knockouts/knockins, by other functional studies, and by testing these genes in human populations (by collaboration). Finally in Aim 3, we propose to study the interactions of QTL genes and how they work with each other to form networks.